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Sample Statement of Purpose (SOP) for MS in Computer Science
While public awareness of sleep related disorders is growing, Sleep Apnea Syndrome remains a
public health and economic challenge. Although various devices have proved useful to measure
physiological signals in apneic events, significant opportunities remain unexplored to enhance
quality and affordability of sleep apnea care. It was during my internship at MIT Media Labs that I
had a chance to employ complex Machine Learning (ML) algorithms in this real-time problem
solving. As a part of ‘Sleep Apnea Project’, I was responsible for snore detection and breath
monitoring of patients, using sensors to build breathing profiles. Making use of multiclass classifier
using Supervised Learning methods, I collected data to identify sleep patterns and experimented
with new technologies like MATLAB, Arduino, Python, and Scikit-learn. As the project concluded, I
successfully developed a low-cost diagnostic sleep mask for detection of Obstructive Sleep Apnea,
which was well appreciated for its ingenuity, portability and ease of use. This experience
significantly piqued my interest in engaging technological tools to alleviate a physical disability and
seamlessly amalgamated with one of my life goals – to solve a conundrum affecting a large subset
of people.
The success of my internship also translated in the successful execution of my final year project on
Automated Surveillance System using Computer Vision. The main objective here was to monitor
and track people’s activities from CCTV footage, for which I proposed a person-classifier using HOG
descriptors. It made use of algorithms like SVM and Convolutional Neural Network supported by
KCF trackers and data association techniques, to address the problem of partial occlusion and
multi-object tracking. As the recorded HD-video required high computational time, we put forth a
GPU implementation of our solution using CUDA, reducing time to nearly one tenth. This project
earned me extensive experience in Machine Learning and formally introduced me to the
convoluted interdependency of hardware with software. My laudatory effort for this study was
published in a leading national daily and motivated me further to delve deeper into core concepts
of intelligent machines.
In order to gain exposure to work on large datasets, I am currently employed at Credit Suisse's (CS)
Algorithmic Trading hub, the Systematic Market-making Group. Starting out as an intern, I worked
on Linux shell scripting and Exchange Simulator using WCF Service, which became my training
ground on web development. In my current profile as a developer on strategic trade-entry
applications, Toolkit and Finscreen, I am instrumental in creating a new feature for auto-loading of
baskets to streamline the process for traders who want to execute basket orders. For efficient low-
latency trading, I built an Asynchronous Logging system by using wait-free producer-consumer
system, which allowed logging in nanosecond times. Working on these assignments significantly
honed my proficiency in Data Structures and Operating Systems concepts like multithreading,
synchronization, and queuing using spin-locks and ring-buffers. My lucrative experience at CS also
enhanced coding abilities and developed flexibility to work with a number of tools and
technologies, opening my eyes to Software Development Process, use of Design Patterns and large-
scale code collaboration.
With such myriad experiences in employing ML on medium scale, I now wish to leverage my
knowledge to build intelligent systems having cognitive abilities. To that end, I want to pursue my
Masters in Computer Science, with a focus on Machine Learning. Owing to my sustained interest in
financial domain, I want to work in Investment Banks like Morgan Stanley and Goldman Sachs, to
develop quant models for equities and derivatives algorithmic trading, using ML and Big Data
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technologies. Other interesting avenues in healthcare to identify solutions in disease control and
redesigning existing diagnostics excite me to the core.
My contributions to the aforementioned projects would not have been possible without a robust
undergraduate computer science curriculum. Applications of Image Processing and Computer
Vision fascinated me, leading to my final year project on the subject of people monitoring.
Probability helped me gain insights into statistics required for Machine Learning. While courses like
Computer Programming, refined my coding skills in C++ and Java and introduced me to the
software domain, Digital Logic Design, Microprocessors Systems, and Embedded Systems taught
me data structures and computer architecture. These not only earned me exposure on the
essentials like OS concepts, memory management, CPU and GPU computing, but also enlightened
me on inherent synergy of software on architecture and hardware. Additionally, I took courses on
ML and Deep learning from Coursera and Udacity to imbibe advanced cognizance.
My academics have always been my top priority, but I do engage in activities, which contribute
positively to my personality. I am quite adept at solving advance Rubik's puzzles like 6x6, 7x7,
Pyraminx, Megaminx, Mirror Cube, and have been a part of several such workshops to teach its
modality to underprivileged children. My solutions oriented approach made me winner at several
hackathons, including the ones by MediaTek Labs and Texas Instruments, with themes aligned to
my research. My leadership skills refined during my roles of social media manager at University
festival and TPO Coordinator of my class. Since it has always been my endeavor to contribute
positively to our community, I have led a recycling drive in association with Green Yatra, devoted
towards protection, conservation and improvement of nature.
Although my experiences with ML to downsize a real world problem have been versatile, I want to
pursue Master of Computer and Information Technology at XXX University to significantly advance
my knowledge in this domain. The courses ‘X’ and ‘Y’ are essential for my career, while ‘Z’
contributes towards my specialization. The possibility of collaborating with distinguished faculty
members like Dr. ABC, to gain valuable insights in computational finance, seems extremely exciting.
With innovative groups like ‘AA’, I intend to become an active member of the ML community by
organizing seminars and annual spotlight sessions. Besides academic, I intend to work closely with
global peers in the Office of Diversity and Inclusion, to widen my perspective on the concept of
inclusive environment. I hope that the admission committee reviews my application in a positive
light and invites me to be a part of the prestigious network of incoming graduate students for Fall-
2020.
